mFrag: Integrating External DNA Methylation References Improves Lung Cancer Early Detection by Fragmentomics
- 1Fred Hutchinson Cancer Center
Abstract
Fragmentomics analyzes features of circulating cell-free DNA fragments (e.g., fragment length or end motifs) derived from whole-genome sequencing of liquid biopsy samples (blood or other body fluids), enabling non-invasive early cancer detection. Here we present mFrag, a framework that integrates external methylation reference atlases with fragmentomic features for lung cancer detection. mFrag comprises two complementary methods: mFrag-Weight, which uses DNA methylation information to weight fragmentomic features, and mFrag-Net, which integrates fragmentomic features and external DNA methylation data through neural networks. We average the output probabilities of the two methods to obtain a final prediction score. The mFrag model was trained using 287 individuals at risk for lung cancer. In an independent case-control testing set comprising 385 non-cancer controls and 46 cancers (28 stage I, 12 stage II, and 6 stage III or IV), mFrag outperforms a DELFI-style fragmentomic approach, improving PR-AUC from 0.455 to 0.720 and sensitivity at 99% specificity from 0.043 to 0.478, with ROC-AUC from 0.871 to 0.908. For stage I lung cancer, mFrag raises PR-AUC from 0.314 to 0.558 and ROC-AUC from 0.855 to 0.894. Applied to end 4-mer motif features, mFrag likewise improved over an unweighted baseline, with gains contributed by both mFrag-Weight and mFrag-Net. In summary, mFrag improves the accuracy of fragmentomics-based lung cancer detection through integrating external DNA methylation data and the complementarity of its two component models, without incurring any additional data-generating cost.
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BibTeX
@misc{cui2026mfrag,
title = {{mFrag: Integrating External DNA Methylation References Improves Lung Cancer Early Detection by Fragmentomics}},
author = {S. Cui and N. Swarup and Q. He and W. Hsu and D. Wong and Y. Zheng and R. B. Scharpf and W. Sun},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.28.762499},
url = {https://www.biorxiv.org/content/10.64898/2026.10.28.762499v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - mFrag: Integrating External DNA Methylation References Improves Lung Cancer Early Detection by Fragmentomics
AU - S. Cui
AU - N. Swarup
AU - Q. He
AU - W. Hsu
AU - D. Wong
AU - Y. Zheng
AU - R. B. Scharpf
AU - W. Sun
PY - 2026
DA - 2026/10/28
PB - bioRxiv
DO - 10.64898/2026.10.28.762499
UR - https://www.biorxiv.org/content/10.64898/2026.10.28.762499v1
AB - Fragmentomics analyzes features of circulating cell-free DNA fragments (e.g., fragment length or end motifs) derived from whole-genome sequencing of liquid biopsy samples (blood or other body fluids), enabling non-invasive early cancer detection. Here we present mFrag, a framework that integrates external methylation reference atlases with fragmentomic features for lung cancer detection. mFrag comprises two complementary methods: mFrag-Weight, which uses DNA methylation information to weight fragmentomic features, and mFrag-Net, which integrates fragmentomic features and external DNA methylation data through neural networks. We average the output probabilities of the two methods to obtain a final prediction score. The mFrag model was trained using 287 individuals at risk for lung cancer. In an independent case-control testing set comprising 385 non-cancer controls and 46 cancers (28 stage I, 12 stage II, and 6 stage III or IV), mFrag outperforms a DELFI-style fragmentomic approach, improving PR-AUC from 0.455 to 0.720 and sensitivity at 99% specificity from 0.043 to 0.478, with ROC-AUC from 0.871 to 0.908. For stage I lung cancer, mFrag raises PR-AUC from 0.314 to 0.558 and ROC-AUC from 0.855 to 0.894. Applied to end 4-mer motif features, mFrag likewise improved over an unweighted baseline, with gains contributed by both mFrag-Weight and mFrag-Net. In summary, mFrag improves the accuracy of fragmentomics-based lung cancer detection through integrating external DNA methylation data and the complementarity of its two component models, without incurring any additional data-generating cost.
ER -